Collectively, the results of both experiments demonstrate that StyleRank is robust to corpora of varying sizes, and highly correlated with human perception of stylistic similarity. In the Appendix, we expand Experiment 1 to demonstrate that StyleRank's performance is robust, even when the number of distinct styles in \(\mathcal {G}\)  is increased. In Experiment 1, there is a large difference between raw distance measures and StyleRank. This highlights the limitations of the approach described by Yang and Lerch, which uses euclidean distance to measure the distance between feature vectors [1]}. Although euclidean distance works well in low-dimensional settings, it does not scale well to high dimensions. In fact, it has been shown that Manhattan distance performs better than Euclidean distance in high dimensional settings [2]}, which we also see in our own experimental results. Understandably, there is a decrease in performance when analyzing styles delineated by genre, as these styles have more variance, and are less consistent than the work of a single composer. Overall, these results demonstrate that StyleRank can proficiently rank MIDI files with different styles.
